A Dispersion Operator for Geometric Semantic Genetic Programming

@InProceedings{Oliveira:2016:GECCO,
author = "Luiz Otavio V. B. Oliveira and
Fernando E. B. Otero and Gisele Lobo Pappa",
title = "A Dispersion Operator for Geometric Semantic Genetic
Programming",
booktitle = "GECCO '16: Proceedings of the 2016 Annual Conference
on Genetic and Evolutionary Computation",
year = "2016",
editor = "Tobias Friedrich",
pages = "773--780",
note = "Best paper",
keywords = "genetic algorithms, genetic programming",
month = "20-24 " # jul,
organisation = "SIGEVO",
address = "Denver, USA",
publisher = "ACM",
publisher_address = "New York, NY, USA",
isbn13 = "978-1-4503-4206-3",
DOI = "doi:10.1145/2908812.2908923",
abstract = "Recent advances in geometric semantic genetic
programming (GSGP) have shown that the results obtained
by these methods can outperform those obtained by
classical genetic programming algorithms, in particular
in the context of symbolic regression. However, there
are still many open issues on how to improve their
search mechanism. One of these issues is how to get
around the fact that the GSGP crossover operator cannot
generate solutions that are placed outside the convex
hull formed by the individuals of the current
population. Although the mutation operator alleviates
this problem, we cannot guarantee it will find
promising regions of the search space within feasible
computational time. In this direction, this paper
proposes a new geometric dispersion operator that uses
multiplicative factors to move individuals to less
dense areas of the search space around the target
solution before applying semantic genetic operators.
Experiments in sixteen datasets show that the results
obtained by the proposed operator are statistically
significantly better than those produced by GSGP and
that the operator does indeed spread the solutions
around the target solution.",
notes = "UFMG, University of Kent
GECCO-2016 A Recombination of the 25th International
Conference on Genetic Algorithms (ICGA-2016) and the
21st Annual Genetic Programming Conference (GP-2016)",
}